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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mechanical behavior of ultralight nickel metamaterial

The mechanical response of ultralight kagomé structures consisting of hollow nickel (Ni) nanotubes and solid Ni nanorods to compression is studied using molecular dynamics simulations. In both kagomé architectures, 16[112] Shockley partial dislocations and twin formation are observed under compression. The structure made from solid nanorods shows deformation near both the nodes and beams of the kagomé lattice. The hollow kagomé architecture has a higher yield point than the solid kagomé structure. The deformation in the hollow nanotube structure is mostly localized in the nodal region for strains less than 11%. At higher strains, the deformation sets in all the struts and nodes of the hollow kagomé lattice. Owing to this two-stage deformation mechanism, the hollow Ni nanotube kagomé structure shows less bending and greater toughness than the solid Ni nanorod kagomé architecture.

Rajak, Pankaj (ORCID:0000000263446056)↗
DOE OSTI2022

Nonaffine Strains Control Ductility of Metallic Glasses

The origin of limited plasticity in metallic glasses is elusive, with no apparent link to their atomic structure. In this study, we propose that the response of the glassy structure to applied stress, not the original structure itself, provides a gauge to predict the degree of plasticity. We carried out high-energy x-ray diffraction on various bulk metallic glasses (BMGs) under uniaxial compression within the elastic limit and evaluated the anisotropic pair distribution function. We show that the extent of local deviation from the affine (uniform) deformation in the elastic regime is strongly correlated with the plastic behavior of BMGs beyond yield, across chemical compositions and sample history. The results suggest that the propensity for collective local atomic rearrangements under stress promotes plasticity.

3-dimensional systems↗
DOE OSTI2021

FAST.Farm load validation for single wake situations at alpha ventus

The main objective of the presented work is the validation of the simulation tool FAST.Farm for the calculation of power and structural loads in single wake situations; the basis for the validation is the measurement database of the operating offshore wind farm alpha ventus. The approach is described in detail and covers the calibration of the aeroelastic turbine model, transfer of environmental conditions to simulations, and comparison between simulations and adequately filtered measurements. It is shown that FAST.Farm accurately predicts power and structural load distributions over wind direction with discrepancies of less than 10 % for most of the cases compared to the measurements. Additionally, the frequency response of the structure is investigated, and it is calculated by FAST.Farm in good agreement with the measurements. In general, the calculation of fatigue loads is improved with a wake-added turbulence model added to FAST.Farm in the course of this study.

17 WIND ENERGY↗
DOE OSTI2020

Crystallizing Atomic Xenon in a Flexible MOF to Probe and Understand Its Temperature-Dependent Breathing Behavior and Unusual Gas Adsorption Phenomenon

Flexible metal–organic frameworks (MOFs) hold great promise as smart materials for specific applications such as gas separation. These materials undergo interesting structural changes in response to guest molecules, which is often associated with unique adsorption behavior not possible for rigid MOFs. Understanding the dynamic behavior of flexible MOFs is crucial yet challenging as it involves weak host–guest interactions and subtle structural transformation not only at the atomic/molecular level but also in a nonsteady state. Here we report an in-depth study on the adsorbate- and temperature-dependent adsorption in a flexible MOF by crystallizing atomic gases into its pores. Mn(ina)2 shows an interesting temperature-dependent response toward noble gases. Its nonmonotonic, temperature-dependent adsorption profile results in an uptake maximum at a temperature threshold, a phenomenon that is unusual. Full characterization of Xe-loaded MOF structures is performed by in situ single-crystal and synchrotron X-ray diffraction, IR spectroscopy, and molecular modeling. The X-ray diffraction analysis offers a detailed explanation into the dynamic structural transformation and provides a convincing rationalization of the unique adsorption behavior at the molecular scale. The guest and temperature dependence of the structural breathing gives rise to an intriguing reverse of Xe/Kr adsorption selectivity as a function of temperature. The presented work may provide further understanding of the adsorption behavior of noble gases in flexible MOF structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Revisiting Multi-Material Composite Structures with Homogenized Composite Properties

Composite structures inherently develop residual stresses during their curing process. Driven predominately by mismatched thermal strains between differing materials or ply orientations, but also affected by curing process phenomena like polymer shrinkage, these residual stresses can lead to failure within composite structures. There are several methods varying in complexity that can be used to model the development of residual stresses, all of which are capable of capturing sufficient detail to understand the residual stress state at the ply level. However, explicitly modeling all plies of a layup in a composite structure can be prohibitively expensive based on the number of plies, structure size, and required element size. The computational cost can be reduced through the homogenization of the composite layup without losing much fidelity of the overall response of the structure. The homogenization process reduces the many plies of a laminate to a single lamina that reduces complexity and increases the mesh size where a single element can span multiple plies. This report focuses on verification and validation efforts for a homogenization process using a suite of finite element simulations rather than an analytic solution derived from classical laminate theory. Initial verification using representative element volumes indicated there was minimal error in the homogenization process; however, this compounded to a small, but acceptable error in strip and split ring experimental composite structures. The error does under predict the residual stress state in the strip and split ring and should be accounted for when simulating composite structures with homogenized properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Response of 11 B enriched ZrB 2 ultra-high temperature ceramic to neutron irradiation at elevated temperatures

ZrB 2 , an ultra-high temperature ceramic (UHTC) is being considered for use in fusion reactor first-wall structures, yet its response to irradiation remains poorly understood. This study employed scanning/transmission electron microscopy (S/TEM), synchrotron X-ray diffraction (XRD), finite element calculations, and thermal property measurements to thoroughly investigate the neutron-irradiation effects on 11 B-enriched ZrB 2 . Neutron irradiations were conducted at 220 °C and 620 °C, with a neutron fluence of 2.2 × 10 25 neutron/m 2 (energy > 0.1 MeV), resulting in 3.9 dpa and 4200 appm He. The study revealed the unusual prevalence of prism loops and a > c anisotropic lattice swelling, likely linked to the low c/a ratio of ZrB 2 , leading to grain boundary microcracking. Reducing the grain sizes was effective in reducing intergranular cracking and macroscopic swelling. The observation of cavities in ZrB 2 irradiated at 620 °C, as opposed to 220 °C, prompts questions about the temperature at which vacancies in ZrB 2 become mobile, and the role of neutron absorption by 10 B in elevating irradiation temperatures. Isotopic enrichment in 11 B proves to be a viable strategy for mitigating helium production in transition-metal diborides, which is a critical consideration for nuclear applications. Irradiation-induced defects reduce the thermal diffusivity and conductivity of ZrB 2 by a factor of 4–9, which has important implications for its role as a plasma-facing material in fusion reactors that drive high heat fluxes through first-wall materials. Here, this comprehensive study lays the foundation for understanding ZrB 2 behavior under neutron irradiation and highlights important phenomena to consider for various material applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A materials data framework and dataset for elastomeric foam impact mitigating materials

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact-mitigating materials used in helmet designs lack open datasets. Here, we describe a new FAIR (findable, accessible, interoperable, reusable) data framework with structural and mechanical response data for one example elastic impact protection foam. The continuum-scale behavior of foams emerges from the interplay of polymer properties, internal gas, and geometric structure. This behavior is rate and temperature sensitive, therefore, describing structure-property characteristics requires data collected across several types of instruments. Data included are from structure imaging via micro-computed tomography, finite deformation mechanical measurements from universal test systems with full-field displacement and strain, and visco-thermo-elastic properties from dynamic mechanical analysis. These data facilitate modeling and design efforts in foam mechanics, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The data framework is implemented using data services and software from the Materials Data Facility of the Center for Hierarchical Materials Design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Understanding the dynamic evolution of atomically dispersed Cu catalyst for CO 2 electrochemical conversion using integrated XANES analysis and mechanistic studies

Direct electrochemical conversion of CO 2 to ethanol (CH 3 CH 2 OH) offers a promising strategy to lower CO 2 emission while storing energy from renewable electricity. Our recent study reported a carbon-supported atomically dispersed Cu catalyst that achieved the highest reported selectivity for CH 3 CH 2 OH formation (91%) at a relatively low potential (-0.6 V), however, the active site structure that is responsible for such high activity and selectivity has yet to be understood. Here, in this paper, we demonstrate a computational investigation combining X-ray absorption near edge structure (XANES) simulations and a mechanistic study via density functional theory (DFT) to understand the catalyst structures of this Cu catalyst during electrocatalysis and the corresponding reaction mechanisms of the key products. An integrated computational and experimental XANES analysis depicted the dynamic evolution of the catalytic site during electrocatalysis. The as-prepared, atomically dispersed Cu catalyst aggregates and forms metallic clusters/nanoparticles under electrochemical condition, which then break down to smaller oxidized clusters after electrocatalysis. The formed Cu clusters/nanoparticles showed distinct catalytic activity and selectivity as a function of particle size based on the mechanistic investigation using DFT, which is consistent with experimental observations for catalyst samples with different Cu loadings. This comprehensive study which combines experimental and computational XANES investigation, mechanistic study via DFT calculations, and experimental performance of the catalysts, provides unprecedented dynamic and mechanistic insights into the supported atomically dispersed metal catalysts for CO 2 reduction. Such strategy and details gained can further guide discovery of novel catalyst materials for CO 2 electrochemical reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2021

Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables

Polarization switching mechanisms in ferroelectric materials are fundamentally linked to local domain structure and the presence of the structural defects, which both can act as nucleation and pinning centers and create local electrostatic and mechanical depolarization fields affecting wall dynamics. However, the general correlative mechanisms between domain structure and polarization dynamics are only weakly explored, precluding insight into the associated physical mechanisms. In this work, the correlation between local domain structures and switching behavior in ferroelectric materials is explored using convolutional encoder–decoder networks, enabling image to spectral (im2spec) and spectral to image (spec2im) translations via encoding of latent variables. The latter reflect the assumption that the relationship between domain structure and polarization switching is parsimonious, i.e., is based upon a small number of local mechanisms. The analysis of latent variables distributions and their real-space representations provides insight into the predictability of the local switching behavior and hence associated physical mechanisms. We further pose that the regions where these correlative relationships are violated, i.e., predictability of the polarization dynamics from domain structure is reduced, represent the obvious target for detailed studies, e.g., in the context of automated experiments. This approach provides a workflow to establish the presence of correlation between local spectral responses and local structure and can be universally applied to spectral imaging techniques such as piezoresponse force microscopy (PFM), scanning tunneling microscopy (STM) and spectroscopy, and electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Air‐Ice‐Ocean Coupling During a Strong Mid‐Winter Cyclone: Observing Coupled Dynamic Interactions Across Scales

Abstract Arctic cyclones are key drivers of sea ice and ocean variability. During the 2019–2020 Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, joint observations of the coupled air‐ice‐ocean system were collected at multiple spatial scales. Here, we present observations of a strong mid‐winter cyclone that impacted the MOSAiC site as it drifted in the central Arctic pack ice. The sea ice dynamical response showed spatial structure at the scale of the evolving and translating cyclonic wind field. Internal ice stress and ocean stress play significant roles, resulting in timing offsets between the atmospheric forcing and the ice response and post‐cyclone inertial ringing in the ice and ocean. Ice motion in response to the wind field then forces the upper ocean currents through frictional drag. The strongest impacts to the sea ice and ocean from the passing cyclone occur as a result of the surface impacts of a strong atmospheric low‐level jet (LLJ) behind the trailing cold front and changing wind directions between the warm‐sector LLJ and post cold‐frontal LLJ. Impacts of the cyclone are prolonged through the coupled ice‐ocean inertial response. Local impacts of the approximately 120 km wide LLJ occur over a 12 hr period or less and at scales of a kilometer to a few tens of kilometers, meaning that these impacts occur at combined smaller spatial scales and faster time scales than most satellite observations and coupled Earth system models can resolve.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Subsystem Estimation: A Modal Decomposition Approach

In structural analysis it is often the requirement that the response of a critical subsystem must be extracted from the response of the overall structure even when not directly excited or sometimes even measured. Based on this requirement, a number of signal processing approaches evolve that can provide a meaningful solution to this troubling problem; however, there is a variety of prior information (parameters or data) that must be available. For instance, knowledge of subsystem bandwidth may be enough to design a simple filter if there is little or no overlap with other component systems. If this is not satisfactory and a specific spectral response is required, then a model-based approach can be applied using an available free-free response of the subsystem from calibration or acceptance tests. However, when assembled and coupled with the overall system, the subsystem boundary conditions have been altered and therefore its response is altered as well.

42 ENGINEERING↗
DOE OSTI2026

Structural and physicochemical insights into pH-responsive poly(DEAEMA- co -HEMA)-grafted mesoporous silica nanoparticles

Mesoporous silica nanoparticles (SiO 2 ) grafted with responsive polymer shells are versatile hybrid systems. Understanding their three-dimensional organization in the hydrated state remains a significant challenge. Here, in this study, SiO 2 nanoparticles were functionalized with a poly(DEAEMA-co-HEMA) shell via a “grafting-from” polymerization strategy in aqueous media. Successful surface modification was confirmed by FTIR, thermogravimetric analysis, transmission electron microscopy, X-ray photoelectron spectroscopy, N 2 sorption, and ζ-potential, yielding grafting contents of 22% (p1 DEAEMA-co-HEMA ) and 49% (p2 DEAEMA-co-HEMA ). Small-angle neutron scattering (SANS) with solvent contrast variation was employed to elucidate the solution-state core–shell architecture of p1 and p2 hybrids at pH 2, where the grafted polymer shell is protonated and highly hydrated. Near contrast matching of the silica core, theoretically estimated at 58% D 2 O, suppressed the scattering intensity, enhancing sensitivity to the polymer shell. Constrained core–shell ellipsoid modeling across solvent contrasts revealed a systematic increase in shell thickness, overall particle dimensions, and shell anisotropy with increasing grafting content. In solution, the hydrated polymer shells were markedly more extended and structurally anisotropic than suggested by dry-state techniques structural characterization, highlighting the importance of solution-state structural analysis for accurately describing grafted polymer architectures.

Core-shell biomaterials↗
DOE OSTI2021

Deciphering Interfacial Chemical and Electrochemical Reactions of Sulfide-Based All-Solid-State Batteries

Large interfacial resistance resulting from interfacial reactions is widely acknowledged as one of the main challenges in sulfide electrolytes (SEs)-based all-solid-state lithium batteries (ASSLBs). However, the root cause of the large interfacial resistance between the SEs and typical layered oxide cathodes is not fully understood yet. Here we deciphered that interfacial oxygen loss from single-crystal LiNi 0.5 Mn 0.3 Co 0.2 O 2 (SC-NMC532) chemically oxidizes Li 10 GeP 2 S 12 , generating oxygen-containing interfacial species. Meanwhile, the interfacial oxygen loss also induces a structural change of oxide cathodes (layered-to-rocksalt). Besides, the high operation voltage can electrochemically oxidize SEs to form non-oxygen species (e.g. polysulfides). These chemically and electrochemically oxidized species, together with the interfacial structural change, are responsible for the large interfacial resistance at the cathode interface. More importantly, the widely adopted interfacial coating strategy is effective in suppressing chemically oxidized oxygen-containing species and mitigating the coincident interfacial structural change but is unable to prevent electrochemically induced non-oxygen species. These findings provide a deeper insight into the large interfacial resistance between the typical SE and layered oxide cathodes, which may be of assistance for the rational interface design of SE-based ASSLBs in future.

25 ENERGY STORAGE↗
DOE OSTI2023

Microstructural and Rheological Transitions in Bacterial Biofilms

Biofilms are aggregated bacterial communities structured within an extracellular matrix (ECM). ECM controls biofilm architecture and confers mechanical resistance against shear forces. From a physical perspective, biofilms can be described as colloidal gels, where bacterial cells are analogous to colloidal particles distributed in the polymeric ECM. However, the influence of the ECM in altering the cellular packing fraction (Φ) and the resulting viscoelastic behavior of biofilm remains unexplored. Using biofilms of Pantoea sp. (WT) and its mutant (ΔUDP), the correlation between biofilm structure and its viscoelastic response is investigated. Experiments show that the reduction of exopolysaccharide production in ΔUDP biofilms corresponds with a seven-fold increase in Φ, resulting in a colloidal glass-like structure. Consequently, the rheological signatures become altered, with the WT behaving like a weak gel, whilst the ΔUDP displayed a glass-like rheological signature. By co-culturing the two strains, biofilm Φ is modulated which allows us to explore the structural changes and capture a change in viscoelastic response from a weak to a strong gel, and to a colloidal glass-like state. The results reveal the role of exopolysaccharide in mediating a structural transition in biofilms and demonstrate a correlation between biofilm structure and viscoelastic response.

viscoelasticity↗
DOE OSTI2023

Merging flux-variance with surface renewal methods in the roughness sublayer and the atmospheric surface layer

In this article, Two micrometeorological methods utilizing high-frequency sampled air temperature were tested against eddy covariance (EC) sensible heat flux (𝐻) measurements at three sites representing agricultural, agro-forestry, and forestry systems. The two methods cover conventional and newly proposed forms of the flux-variance (FV) and surface renewal (SR) schemes of differing complexities. The sites represent measurements in surface, roughness, and roughness to surface transitional layers. Regression analyzes against EC show that the most reliable FV and SR forms estimate 𝐻 with slopes within ±10% from unity and coefficient of determination 𝑅2 > 0.9 across all the three sites. The best performance of both FV and SR was found at the agricultural site with measurements well within the surface layer, while the worst was found for the tall forest with measurements within the roughness sublayer where its thickness needed to be additionally estimated. The main variable driving 𝐻 in FV is the temperature variance, whereas in SR, it is the geometry of ramp-like structures. Since these structures are also responsible for most of the temperature variance, a novel FV-SR approach emerging from combining the methods is proposed and evaluated against EC measurements and conventional FV and SR schemes. The proposed FV-SR approach requiring only a single fast response thermocouple is potentially independent of calibration and ameliorates some of the theoretical objections that arise when combining ramp statistics with similarity arguments. The combination of methods also provides new insights into the contribution of coherent structures to the temperature variance and its dependence on atmospheric stratification. Other potential utility of the new method is to include it in multi-tool assessments of surface energy fluxes, since a convergence or divergence of the results has a high diagnostic value.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Development, verification, and validation of comprehensive acoustic fluid-structure interaction capabilities in an open-source computational platform

The acoustic fluid-structure interaction (FSI) formulation is a practical numerical approach for the seismic analysis of fluid-filled tanks. However, there are no verification and validation studies reported in the literature that demonstrate the ability of an acoustic FSI numerical model to predict responses important to structural and mechanical design for intense translational and rotational earthquake inputs. Herein, an acoustic FSI formulation is implemented in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE), and is formally verified and validated using analytical solutions and code-to-code verification, and experimental data, respectively. The analytical solutions are for small amplitude, unidirectional seismic inputs. The code-to-code verification utilizes a previously verified and validated Arbitrary Lagrangian-Eulerian (ALE) numerical model in the commercial finite element code LS-DYNA. The validation studies utilize a comprehensive data set assembled from results of 3D earthquake-simulator tests of a fluid-filled vessel. The acoustic numerical model in MOOSE is verified and validated for hydrodynamic pressures and support reactions except for cases that involve significant convective response. For small amplitude inputs, numerically predicted wave heights match those of the analytical solutions. The numerical model is not verified and validated for wave height calculations under intense 3D seismic inputs. The run times for the acoustic FSI simulations in MOOSE are an order of magnitude, or more, shorter than for the corresponding ALE simulations in LS-DYNA. The utility of the MOOSE acoustic FSI implementation is demonstrated by seismic analysis of a building equipped with a fluid-filled, advanced nuclear reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗